利用概率的位置匿名算法
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摘要
k匿名模型是一种有效的位置隐私保护技术,通过构造包含需要保护的用户在内的k个正在发送请求的用户的匿名区域,达到保护用户位置的目的.但是现有的k匿名模型仅能利用当前正在发送请求的用户,当同时发送请求用户较少时,就会导致匿名区域过大.为此,提出一种利用概率的位置匿名算法来保护路网中的移动用户的位置,利用当前时刻的不活跃用户的历史位置轨迹,计算出进入匿名路段的概率,可明显减小匿名路段长度.实验结果证明,基于概率的位置匿名算法与一般的k匿名模型相比较,提高了匿名效率.
As one of the most effective location privacy preservation technologies,the k-anonymity model provides safeguards for location privacy of the mobile client against vulnerabilities for abuse by constructing an anonymous area of k users including the protected one.However,most existing k-anonymity models only utilize the users who are sending requests at recent time.If there are not enough requesting users,the generated anonymous area of the k-anonymity model will be larger than expected.In this paper,a Probability-based Location Anonymity(PLA)algorithm is proposed for protecting location privacy of the mobile users in a road network.The PLA model takes advantage of the historical path track of the users who are not sending the request currently,and then computes the probability into the anonymous section so that it can greatly reduce the size of the anonymous area.Experimental results show that the PLA algorithm is superior to the k-anonymity and it increases its anonymous efficiency enormously.
As one of the most effective location privacy preservation technologies,the k-anonymity model provides safeguards for location privacy of the mobile client against vulnerabilities for abuse by constructing an anonymous area of k users including the protected one.However,most existing k-anonymity models only utilize the users who are sending requests at recent time.If there are not enough requesting users,the generated anonymous area of the k-anonymity model will be larger than expected.In this paper,a Probability-based Location Anonymity(PLA)algorithm is proposed for protecting location privacy of the mobile users in a road network.The PLA model takes advantage of the historical path track of the users who are not sending the request currently,and then computes the probability into the anonymous section so that it can greatly reduce the size of the anonymous area.Experimental results show that the PLA algorithm is superior to the k-anonymity and it increases its anonymous efficiency enormously.
引文
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[13]Karimi H A,Liu X.A Predictive Location Model for Location-based Services[C]//Proceedings of the 11th ACM International Symposium on Advances in Geographic Information Systems.New York:ACM,2003:126-133.
[14]Thomas Brinkhoff:Network-based Generator of Moving Objects[EB/OL].[2014-05-20].http://iapg.jade-hs.de/personen/brinkhoff/generator/.
[2]Wang Y,Xu D,He X.L2P2:Location-aware Location Privacy Protection for Location-based Services[C]//Proceedings of IEEE INFOCOM.Piscataway:IEEE,2012:1996-2004.
[3]Wang Q,Xu Z,Qu S.An Enhanced K-Anonymity Model Against Homogeneity Attack[J].Journal of Software,2011,6(10):1945-1952.
[4]Gruteser M,Grunwald D.Anonymous Usage of Location-based Services through Spatial and Temporal Cloaking[C]//Proceedings of the 1st International Conference on Mobile Systems,Applications and Services.New York:ACM,2003:31-42.
[5]Beresford A R,Stajano F.Location Privacy in Pervasive Computing[J].IEEE Pervasive Computing,2003,2(1):46-55.
[6]Kim M,Fielding J J,Kotz D.Risks of Using AP Locations Discovered through War Driving in Pervasive Computing[M].Berlin:Springer,2006:67-82.
[7]Xu T,Cai Y.Exploring Historical Location Data for Anonymity Preservation in Location-based Services[C]//Proceedings of the 27th IEEE Communications Society Conference on Computer Communications.Piscataway:IEEE,2008:1220-1228.
[8]Chow C Y,Mokbel M F.Trajectory Privacy in Location-based Services and Data Publication[J].ACM SIGKDD Explorations Newsletter,2011,13(1):19-29.
[9]Suzuki A,Iwata M,Arase Y,et al.A User Location Anonymization Method for Location Based Services in a Real Environment[C]//Proceedings of the 18th ACM International Symposium on Advances in Geographic Information Systems.New York:ACM,2010:398-401.
[10]Kato R,Iwata M,Hara T,et al.A Dummy-based Anonymization Method Based on User Trajectory with Pauses[C]//Proceedings of the 20th ACM International Symposium on Advances in Geographic Information Systems.New York:ACM,2012:249-258.
[11]Yigitoglu E,Damiani M L,Abul O.Privacy-preserving Sharing of Sensitive Semantic Locations under Road-network Constraints[C]//Proceedings of the 13th IEEE International Conference on Mobile Data Management.Washington:IEEE Computer Society,2012:186-195.
[12]Pan X,Xu J,Meng X.Protecting Location Privacy against Location-dependent Attacks in Mobile Services[J].IEEE Transactions on Knowledge and Data Engineering,2012,24(8):1506-1519.
[13]Karimi H A,Liu X.A Predictive Location Model for Location-based Services[C]//Proceedings of the 11th ACM International Symposium on Advances in Geographic Information Systems.New York:ACM,2003:126-133.
[14]Thomas Brinkhoff:Network-based Generator of Moving Objects[EB/OL].[2014-05-20].http://iapg.jade-hs.de/personen/brinkhoff/generator/.